Grants for Private institutions of higher education - Federal

This grant provides funding to support community-driven educational initiatives that enhance college and career readiness for Indian students through culturally relevant approaches and partnerships with local tribes and educational institutions.

The objective of the Data Science Corps program is to help build a strong national data science infrastructure and workforce. The Data Science Corps program seeks to engage data science students in real-world data science implementation projects. This engagement will help bridge the data-to-knowledge gap in organizations and communities at all levels, including local, state, and national, and will empower better use of data for more effective decision making. Data Science Corps participants will be able to sharpen their skills in data science by working on real-world projects focused on specific community needs, including rural communities, urban communities, academia, industry, or government. This partnership between communities and data scientists will serve the nation by helping produce a workforce-ready cohort of data scientists and technologists, who have experience with data science in action in real-world settings. The program welcomes proposals that seek to broaden participation inscience, technology, engineering and mathematics (STEM) and STEM education. This solicitation prompts the community to respond to one or more mechanisms by which to provide students with data science education and training, including in data science issues related to knowledge representation and creation and use of knowledge graphs. The solicitation supports opportunities for undergraduates (including students from community colleges, Minority Serving Institutions, other emerging research institutions as defined in the CHIPS and Science Act, https://www.congress.gov/bill/117th-congress/house-bill/4346, and institutions in EPSCoR jurisdictions), and grade 6-12 teachers and students. When responding to this solicitation, even though proposals must be submitted through theDirectorate for STEM Education, Division of Research on Learning in Formal and Informal Settings (EDU/DRL), once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors This solicitation grew out of the NSF-wide activity known as Harnessing the Data Revolution (HDR), a national-scale activity to enable new modes of data-driven discovery addressing fundamental questions at the frontiers of science and engineering. HDR has supported an interrelated set of efforts in foundations of data science; data-intensive research in science and engineering; and education and workforce development. Contact Information: Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact. General inquiries may be addressed to [email protected].

This grant provides funding to colleges and universities for projects that improve organic farming practices, enhance soil health, and support farmers transitioning to organic methods.

This grant provides funding for organizations to develop advanced neutrino detectors using quantum sensing technologies, targeting improved detection capabilities for neutrinos from various sources, including nuclear reactors.

This funding opportunity provides financial support for research at minority-serving institutions to explore how structural racism affects neurocognition and substance use disorder risk, with the goal of developing effective prevention strategies.

The Bureau of International Narcotics and Law Enforcement Affairs of the U.S. Department of State announces an open competition for organizations to submit applications to carry out a project to strengthen community policing practices in Uzbekistan. The Government of Uzbekistan has continued its broad reform efforts that began in earnest in 2017, including efforts to modernize the countrys security services. Despite reforms, public trust in security forces remains low, and allegations of corruption, unresponsiveness, and abuse persist. Civil society, which plays an important role in mobilizing citizenry to advocate for better community conditions, remains fragmented and generally lacks support and social capital to push for systemic change. This project will both strengthen Uzbek civil society organizations as they engage in community-based advocacy while also helping local government actors, especially the Ministry of Internal Affairs, develop protocols and procedures to be more transparent, responsive, and communicative in their relations with local communities. Goals of the project Enhance Civil Society Capacity for Community-Based Advocacy Strengthen Government Accountability and Transparency in Local Communities Foster Collaboration and Partnership Between Civil Society and Government Actors

The FY24 MRP Idea Award supports innovative, untested, exploratory, high-risk/potentially high-reward concepts, theories, paradigms, and/or methods that address at least one of the FY24 MRP Focus Areas in Section II.A.1. Key aspects of the Idea Award: The intent of the Idea Award is to generate novel research avenues for investigation; therefore, novelty and innovation should be key aspects of the proposed research. Research supported by the Idea Award must introduce a new paradigm, challenge existing paradigms, look at existing problems from new perspectives, or exhibit other highly creative qualities. The proposed project must be exploratory, hypothesis-driven, or hypothesis-generating research and be based on a well-developed study design and plan of analysis. Principal Investigators (PIs) new to the melanoma field are encouraged to apply. The Idea Award is NOT intended to expand or extend previously published findings or continue a line of research already established and /or funded in the PIs laboratory. Incremental advances, the next logical step, or merely switching the object or method of inquiry from one cancer to melanoma is not considered innovative. The expected outcome of research supported by this award is the generation of robust preliminary data to be used as a foundation for future melanoma-focused research projects. Inclusion of preliminary data is discouraged. PIs proposing projects already supported by significant preliminary data and/or other funding sources should consider applying to other FY24 MRP funding opportunities for which the inclusion of preliminary data is more appropriate or required. Inclusion of preliminary data other than serendipitous findings is not consistent with the exploratory/innovative nature of this award. If preliminary data are included, they should be unanticipated outcomes or results from an unrelated project or study.

This funding opportunity is designed to support Norwich University in developing programs that enhance cybersecurity education and workforce development for military and civilian needs, including outreach to K–12 students.

This funding opportunity provides financial support for state and local governments, tribes, educational institutions, and nonprofit organizations to conduct chemical analysis of fish tissue samples in the Great Lakes, focusing on monitoring contaminants and improving environmental health.

The U.S. Department of State, Bureau of Democracy, Human Rights and Labor (DRL) announces an open competition for a project advances responsive, accountable, and rights-respecting governance for citizens by strengthening environmental governance in Pacific Island countries.

This funding opportunity provides financial support to not-for-profit organizations, NGOs, and educational institutions to promote U.S. exports and improve trade relations in Bangladesh by enhancing market access and fostering a transparent business environment.

This funding opportunity provides financial support for organizations to create and share resources that improve community policing practices and strengthen trust between law enforcement and the communities they serve.

This competition provides $100,000 in funding to individuals and organizations in the U.S. to develop innovative electric power distribution systems that can adapt to modern energy challenges in rural, suburban, and urban settings.

This funding opportunity provides financial support for research projects that aim to reduce health disparities in rural communities by implementing targeted interventions and engaging local resources.

This grant provides funding for research aimed at identifying and overcoming financial and non-zoning obstacles to increasing the supply of diverse housing options, known as Missing Middle Housing, in communities across the United States.

This grant provides funding for organizations to empower women in Eastern Slovakia by equipping them with essential business skills and resources to successfully launch and grow their own businesses.

The U.S. Department of State Bureau of Democracy, Human Rights, and Labor (DRL) announces an open competition for an evaluation project that contributes to building a portfolio of evidence regarding inclusive human rights in Ukraine. In support of this goal, DRL seeks to promote the use of sound data and generate findings on what works, for whom, and why in democracy and human rights programming abroad. Projects should support the following objective: the rights of marginalized, underserved, and at-risk individuals in Ukraine are recognized and respected within a democracy, rights, and good governance framework.

The "OSERS-OSEP: Personnel Development to Improve Services and Results for Children with Disabilities--Preparation of Special Education, Early Intervention, and Related Services Leadership Personnel" grant is a funding opportunity from the U.S. Department of Education aimed at increasing the number of qualified doctoral-level professionals who can lead in special education, early intervention, and related services, with a focus on promoting equity and addressing personnel shortages in these fields.

This program provides funding for U.S. and Chilean non-profit organizations, educational institutions, and individuals to implement projects that strengthen ties between the United States and Chile in areas like combating disinformation, promoting economic growth, and supporting democratic institutions.

Scientific observations, experiments, and simulations are producing data at rates beyond our capacity to store, analyze, stream, and archive the data in raw form. Of necessity, many research groups have already begun reducing the size of their data sets via techniques such as compression, reduced order models, experiment-specific triggers, filtering, and feature extraction. Once reduced in size, transporting, storing, and analyzing the data is still a considerable challenge a reality that motivates SCs Integrated Research Infrastructure (IRI) program [1] and necessitates further innovation in data-reduction methods. These further efforts should continue to increase the level of mathematical rigor in scientific data reduction to ensure that scientifically-relevant constraints on quantities of interest are satisfied, that methods can be integrated into scientific workflows, and that methods are implemented in a manner that inspires trust that the desired information is preserved. Moreover, as the scientific community continues to drive innovation in artificial intelligence (AI), important opportunities to apply AI methods to the challenges of scientific data reduction and apply data-reduction techniques to enable scientific AI, continue to present themselves [2-4].The drivers for data reduction techniques constitute a broad and diverse set of scientific disciplines that cover every aspect of the DOE scientific mission. An incomplete list includes light sources, accelerators, radio astronomy, cosmology, fusion, climate, materials, combustion, the power grid, and genomics, all of which have either observatories, experimental facilities, or simulation needs that produce unwieldy amounts of raw data. ASCR is interested in algorithms, techniques, and workflows that can reduce the volume of such data, and that have the potential to be broadly applied to more than one application. Applicants who submit a pre-application that focuses on a single science application may be discouraged from submitting a full proposal.Accordingly, a virtual DOE workshop entitled Data Reduction for Science was held in January of 2021, resulting in a brochure [5] detailing four priority research directions (PRDs) identified during the workshop. These PRDs are (1) effective algorithms and tools that can be trusted by scientists for accuracy and efficiency, (2) progressive reduction algorithms that enable data to be prioritized for efficient streaming, (3) algorithms which can preserve information in features and quantities of interest with quantified uncertainty, and (4) mapping techniques to new architectures and use cases. For additional background, see [6-9].The principal focus of this FOA is to support applied mathematics and computer science approaches that address one or more of the identified PRDs. Research proposed may involve methods primarily applicable to high-performance computing, to scientific edge computing, or anywhere scientific data must be collected or processed. Significant innovations will be required in the development of effective paradigms and approaches for realizing the full potential of data reduction for science. Proposed research should not focus only on particular data sets from specific applications, but rather on creating the body of knowledge and understanding that will inform future scientific advances. Consequently, the funding from this FOA is not intended to incrementally extend current research in the area of the proposed project. Rather, the proposed projects must reflect viable strategies toward the potential solution of challenging problems in data reduction for science. It is expected that the proposed projects will significantly benefit from the exploration of innovative ideas or from the development of unconventional approaches. Proposed approaches may include innovative research with one or more key characteristics, such as compression, reduced order models, experiment-specific triggers, filtering, and feature extraction, and may focus on cross-cutting concepts such as artificial intelligence or trust. Preference may be given to pre-applications that include reduction estimates for at least two science applications.